Process abnormality identification using measurement violation analysis

ABSTRACT

The subject matter of this specification can be implemented in, among other things, a method, system, and/or device to receive current metrology data for an operation on a current sample in a fabrication process. The metrology data includes a current value for a parameter at each of one or more locations on the current sample. The method further includes determining a current rate of change of the parameter value for each of the one or more locations. The current rate of change is associated with the current sample. The method further includes identifying one or more violating locations each having an associated current rate of change of the parameter value that is greater than an associated reference rate of change of the parameter value, and identifying an instance of abnormality of the fabrication process based on the one or more violating locations.

RELATED APPLICATIONS

This application is a continuation application of co-pending U.S. patentapplication Ser. No. 17/163,101, filed Jan. 29, 2021, which isincorporated herein by reference.

TECHNICAL FIELD

Some embodiments of the present invention relate, in general, tosystems, methods, and devices to detect abnormalities in a manufacturingsystem using sample measurement violation analysis.

BACKGROUND

In manufacturing, for example in semiconductor device fabrication,product quality can be measured directly using metrology tools andindirectly by monitoring process equipment sensors. This information iscollected at different times in the product manufacturing lifecycle.When a manufacturing engineer needs to identify a problem with a processtool or a resulting product, he or she has to go through a laborious andcostly process of analyzing lots of data points (e.g. metrology data ofmany samples with various measured parameters). For example, when anengineer is notified of a potential problem with a product, the engineerhas to review corresponding metrology data to find an alarmingcharacteristic of the product. A common way of identifying metrologyviolations is using statistical process control (SPC).

Statistical process control (SPC) is a method of quality control whichemploys statistical methods to monitor and control a process. SPC canhelp to ensure that the process operates with controlled variation,producing more specification-conforming products with less waste (e.g.,rework or scrap). SPC can be applied to various processes where theconforming product (e.g., product meeting specifications) output can bemeasured. SPC may include industry-standard methodology for measuringand controlling quality during the manufacturing process. Quality datain the form of product and process measurements may be obtained inreal-time during manufacturing. The data can then be plotted on a graphwith calculated control limits. Two limits often used to bound the datainclude, first, control limits, that can be determined by the capabilityof the process and, second, specification limits, that can be determinedby a desired outcome (e.g., a range of measurements meeting certainspecification requirements).

SUMMARY

A method and a system for identifying an instance (e.g. a source) ofabnormality of a fabrication process. The method includes receivingcurrent metrology data for an operation on a current sample in afabrication process. The metrology data includes a current value for aparameter at each of one or more locations on the current sample as wellas samples from prior metrology process steps. The method includesobtaining a reference rate of change of the parameter value of theparameter for each of the one or more locations. The method furtherincludes determining a current rate of parameter change of the parameterfor each of the one or more locations. The current rate of change isassociated with the current sample. The method further includescomparing the current rate of change of the parameter value to thereference rate of change of the parameter value and identifying aninstance of abnormality of the fabrication process based on thecomparison.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereferences indicate similar elements. It should be noted that differentreferences to “an” or “one” embodiment in this disclosure are notnecessarily to the same embodiment, and such references mean at leastone.

FIG. 1 is a block diagram illustrating a manufacturing system, accordingto aspects of the present disclosure.

FIG. 2 is a block diagram illustrating a fabrication process of amanufacturing system, according to aspects of the present disclosure.

FIG. 3 illustrates a rate of change of the parameter value distributionof various sample for an operation in a fabrication process, accordingto aspects of the present disclosure.

FIG. 4 illustrates an exemplary graphical user interface for a samplepattern, according to aspects of the present disclosure.

FIG. 5 is a block diagram illustrating a process failure mode andeffects analysis (FMEA), according to aspects of the present disclosure.

FIG. 6 is a flow diagram of a method for identifying an instance ofabnormality of a fabrication process, according to aspects of thepresent disclosure.

FIG. 7 illustrates a block diagram of an example computing devicecapable of identifying instances of abnormality of a fabricationprocess.

DETAILED DESCRIPTION

In fabrication (e.g., micro-fabrication, waver manufacturing, substrategeneration, and/or the like), various processing tools and proceduresare used to create a desired outcome (e.g. a sample meeting a desiredspecification or having desired properties.) A fabrication process mayinclude various manufacturing steps and metrology steps. Metrology stepscan be used in between manufacturing steps to take metrology data thatis indicative of the quality and success of the manufacturing steppreviously performed. The metrology data may consist of multiplemeasured parameter site values (e.g., thickness, roughness, depth,particle count, surface gradient, etc.) measured at various locations ona wafer comprising a sample. As a sample progresses through afabrication process, historical metrology data can be taken from manymetrology steps and stored to show how the sample has reached thecurrent parameter values. A fabrication system may flag measurementsthat are in violation of a standard. For example, in SPC, a measurementmay be in violation if it falls outside a control limit, violates apredetermined rule, falls outside a specification limit, or themeasurement is otherwise deemed unacceptable. Violating measurements maybe caused by a faulty fabrication machine or process, among otherthings. For example, a fabrication machine may include a broken tool ora wore down instrument that is not performing up to par. Identifying thesource of the violating measurement can be costly and illusive. Forexample fabrication downtime, fabricating inadequate products, and/orthe overcall cost to identity and remedy the source of a fabricationabnormality may be costly and resource intensive.

Conventionally, SPC violation analysis is performed manually. A user maybe presented with SPC charts having identified measurement violations.The user is tasked, through the knowledge of the user, to identify anyabnormalities of the fabrication process, including machines, devices,procedures, etc. that may be defective. However, as fabrication systemsbecome more complex and increasingly automated, the ability to measureand take more data advances, the amount of data becomes intractable toprocess by the user depending only on their own knowledge. Additionally,fabrication systems can be designed to handle many different processes(e.g., various substrate recipes and diverse samples that use a varietyof machines, devices, and/or procedures to reach the fabricated result),which require knowledge and memory beyond what can be performed in thehuman mind. Additionally, determining abnormalities based on SPC resultsis insufficient to determine upstream faults of machines and/orprocesses that were used or carried out in association with an operationof the fabrication process prior to the current or final operation ofthe fabrication process. For example, a first machine may be the sourceof the abnormality, but a process may use 20 machines, after which thesource of the error may be masked or diluted making it difficult toidentify the source.

Aspects and implementations of the present disclosure address these andother shortcoming of existing technology by identifying an instance ofabnormality (e.g., a faulty machine, defective operation, worn out tool,etc.) of a fabrication process using measurement data of a sampleassociated with the fabrication process. Initially, a processing devicemay receive current metrology data for an operation on a current samplein a fabrication process. The metrology data may include a current valuefor a parameter at each of one or more locations on the current sample.The processing device may obtain a reference rate of change of theparameter value of the parameter for each of the one or more locations.The current rate of change of the parameter value may be associated withthe current sample. The processing device may further include comparingthe current rate of change of the parameter value to the reference rateof change of the parameter value and identifying an instance ofabnormality of the fabrication process based on the comparison.

Aspects of the present disclosure result in technological advantages ofsignificant reduction in energy consumption (e.g., battery or powerconsumption), bandwidth, latency, and so forth. For example, byprocessing and analyzing metrology data in the disclosed manner, datacan be processed quicker than conventional systems and allow for moreefficient data storage and acquisition than conventional systems.Additionally, recommended corrective action to be taken to remedyabnormalities can be identified and applied sooner than in conventionalsystems, which can result in reduced costs from fabricating defective ormalfunctioning samples prior to being corrected, preventing furtherdamage from worn, broken, or otherwise defective equipment, andpredicting abnormalities, defects, faults, and/or errors that may occurin the near future and proactively taking corrective action.

FIG. 1 is a block diagram illustrating a manufacturing system 100,according to aspects of the present disclosure. As shown in FIG. 1 ,manufacturing system 100 includes a manufacturing execution system 102,a metrology system 110, a statistical process control (SPC) system 116,a data store 122, a client device 128, and an equipment engineeringsystem 130. The manufacturing execution system 102, the metrology system110, the SPC system 116, the data store 122, and/or the equipmentengineering system 130 can each be hosted by one or more computingdevices including server computers, desktop computers, laptop computers,tablet computers, notebook computers, personal digital assistants(PDAs), mobile communications devices, cell phones, smart phones,hand-held computers, or similar computing devices.

The manufacturing execution system 102, the metrology system 110, theSPC system 116, the data store 122, the equipment engineering system130, and the client device 128 may be coupled to each other via anetwork 140 for identifying instances of abnormality in manufacturingexecution system 102. In some embodiments, network 140 is a publicnetwork that provides each element of manufacturing system 100 withaccess to each other and other publically available computing devices.In some embodiments, network 140 is a private network that provides eachelement of manufacturing system 100 with access to each other and otherprivately available computing devices. Network 140 may include one ormore wide area networks (WANs), local area networks (LANs), wirednetwork (e.g., Ethernet network), wireless networks (e.g., an 802.11network or a Wi-Fi network), cellular network (e.g., a Long TermEvolution (LTE) network), routers, hubs, switches, server computers,and/or combination thereof. Alternatively or additionally, any of theelements of the manufacturing system 100 may be integrated together orotherwise coupled without the use of network 140.

The client device 128 may be or include any personal computers (PCs),laptops, mobile phones, tablet computers, netbook computers, networkconnected televisions (“smart TV”), network-connected media players(e.g., Blu-ray player), a set-top-box, over-the-top (OOT) streamingdevices, operator boxes, etc. The client device 128 may include abrowser, an application, and/or a measurement violation tool. In someembodiments, the client device 128 may be capable of accessing themanufacturing execution system 102, the metrology system 110, the SPCsystem 116, the data store 122, and/or the equipment engineering system130 (e.g., through network 140 using the browser or application) andcommunicating (e.g. transmitting and/or receiving) indications ofviolating measurements, identified abnormalities, and metrology data atvarious stages of the violation analysis, as described herein.

Manufacturing execution system 102 may include machine operations 104,process implementations 106, and process dependencies 108 for variousfabrication processes. A fabrication process may include various stepsor operations that utilize one or more machines to perform one or moremachine operations 104 using one or more different processimplementations 106. For example, various machines may includespecialized chambers such as etch chambers, deposition chambers(including chambers for atomic layer deposition, chemical vapordeposition, or plasma enhanced versions thereof), anneal chambers,and/or the like. In another example, machines may incorporate sampletransportation systems (e.g., a selective compliance assembly robot arm(SCARA) robot, transfer chambers, front opening pods (FOUPs), sidestorage pod (SSP), and/or the like) to transport a sample betweenmachines and process steps.

Process implementations 106 may include various specifications forperforming a machine operation 104 in a fabrication process. Forexample, a process implementation 106 may include process specificationssuch as duration of the machine operation 104, the machine tool used forthe operation, the temperature, flow, pressure, etc. of a machine (e.g.,a chamber), order of deposition, and the like. In another example,process implementations may include transferring instructions fortransporting a sample to a further process step or to be measured bymetrology system 110.

Process dependencies 108, also referred to as fabrication recipes orfabrication process instructions, include an ordering of machineoperations 104 with process implementations 106 that when applied in adesignated order create a fabricated sample (e.g., a substrate or waferhaving predetermined properties or meeting predeterminedspecifications). In some embodiments, the process dependencies 108 of afabrication process are stored in a data store or, alternatively oradditionally, stored in a manner to generate a table of data indicativeof the steps or operations of the fabrication process. For example, oneor more of the metrology system 110, SPC system 116, and/or equipmentengineering system 130 may request a process dependency for a samplecurrently being measured or for whom associated metrology data is beingprocessed. In a further example, a process dependency can be requestedfor a specified number of steps (e.g., the last 5, 10, 15, or n processsteps of a fabrication process) prior to an operation on a currentsample in a fabrication process.

In some embodiments, the manufacturing execution system 102 includes allthe instructions, machinery, and processes to fabricate a sample (e.g.,a substrate or waver), in other embodiments, the manufacturing executionsystem 102 controls and coordinates various machines, tools, andprocesses in fabricating a sample. In other embodiments, themanufacturing execution system 102 combined with the metrology system110 may form one system that is designed to alternate betweenmanufacturing processes and metrology processes throughout a fabricationprocess.

Metrology system 110 includes metrology tools 114 for measuringparameters at various locations on samples being fabricated by themanufacturing execution system 102. The parameters may include variousmeasured values indicative of a quality of an operation performed by afabrication process by the manufacturing execution system 102. Forexample, a parameter may include thickness, etch rate, resistivity,particle count, etc. of a sample. In some embodiments, the metrologytools coordinate measurements to be taken at specific predeterminedlocations on each sample. For example, after a specific fabricationoperation (e.g., a deposition or etch operation), each sample is takento the metrology system 110. The metrology tools 114 measure one or moreparameters at the same locations for each sample. The metrology system110 may transfer the data to the SPC system 116 to make a historicalrecord of the measurements associated with a specific operation of afabrication process. The historical sample data 124 may be used by themetrology system 110 and/or the SPC system 116 to create a record ofparameter measurements over a range of samples all being process afterthe same operation of the fabrication process.

In some embodiments, the samples fabricated with the manufacturingexecution system 102 are measured by the metrology system 110 afterevery step of a fabrication recipe. In some embodiments, the processdependencies 108 or fabrication recipes may include measurement stepssuch that every sample is fabricated with the same process dependencies108 and every sample is measured under the same conditions following thesame operations.

As shown in FIG. 1 , statistical process control (SPC) system 116 mayinclude statistical process tools 118 and an SPC data store such as anSPC database 120. The SPC system 116 receives metrology data frommetrology system 110 and/or equipment engineering system 130. The SPCsystem may receive context data (e.g., channel ID, equipment ID,product, process name process step number lot ID, etc.) frommanufacturing execution system 102, metrology system 110, data store122, and/or equipment engineering system 130. The SPC system 116 appliesthe metrology data to appropriate charts (e.g., thickness, etch rate,resistivity, particle count, etc), analyzes the metrology data to detectviolations (e.g., product characteristics that are above or belowpredefined thresholds), generates information about a product having adetected violation (e.g., a lot ID, a wafer ID, a recipe name), andprovides this information to the equipment engineering system 130. Inaddition, the SPC system 116 presents SPC charts to SPC clients, such asclient device 128. For example the SPC charts may be presented in clientapplications and/or web-based browser applications hosted by clientdevice 128 such as a personal computer, laptop, mobile phone, etc.

The SPC database 120 includes historical metrology data. For example,historical metrology data may include past SPC charts (e.g., thickness,etch rate, resistivity, particle count, etc.) and measurement violationdata. The SPC database 120 may calculate a reference rate of parameterchange 121 of historical sample data 124. For example an analysis ofvariance (ANOVA) may be calculated for one or more historical rates ofchange of samples measured by metrology system 110 after beingfabricated by manufacturing execution system 102. In some embodiments,the SPC system 116 stores the reference rates of change 121 of theparameter value and historical sample data, and in other embodiments,the SPC system 116 stores the rate of change of the parameter value andhistorical sample data in data store 122.

Data store 122 may be a memory (e.g., random access memory), a drive(e.g., a hard drive, a flash drive), a database system, or another typeof component or device capable of storing data. Data store 122 may storeone or more of historical sample data 124 and failure mode and effectsanalysis (FMEA) data 126. The historical sample data 124 may includeparameter values for various parameters measured from a samplefabricated during various fabrication processes (e.g., fabricationprocess 200 of FIG. 2 ). The historical sample data 124 may includereference rates of change of the parameter value for each parameter ateach location on one or more previous samples, previously fabricatedduring a fabrication process. The FMEA data 126 may store dataassociated with the failure mode and effects analysis tool 138 andprocess failure mode and effects analysis more generally (see e.g.,process failure mode and effects analysis 500 of FIG. 5 ).

The equipment engineering system 130 may include a measurement violationtool 132. The measurement violation tool includes a process dependencytool 134, a rate of change tool 135, a pattern mapping tool 137, and afailure mode and effects analysis (FMEA) tool 138. The equipmentengineering system 130 receives metrology data from the metrology system110 and/or the manufacturing execution system 102 and sends themetrology data to the SPC system 116. The equipment engineering system130 receives SPC data associated with the machine, tool, and/or processoperations of the fabrication process associated with the metrologydata.

The process dependency tool 134 requests process dependency 108 data,from the manufacturing execution system 102 and/or the data store 122,associated with the metrology data taken by metrology tools 114. Theprocess dependency tool 134 matches prior machine operations 104 andprocess implementations 106 to measurements of a current sample.

The rate of change tool 135 calculates a current rate of change of acurrent sample being measured by metrology system 110. The current rateof change may include a determination of a current parameter value rateof change. For example, a parameter is measured by metrology system 110and a set of parameter values are obtained at various locations across asample. These parameter values are compared against historical parametervalues to determine how much the current parameter values have changed.In some embodiments, the rate of change of the parameter value iscalculated by comparing the current parameter values to a sample thatwas fabricated immediately previous to the current sample. Thedifference between the current parameter values and parameter valuestaken from the sample fabricated immediately previous to the currentsample may yield a rate of change of the parameter value. In otherembodiments, multiple historical measurements are used with various rateof change of the parameter value calculation techniques. For example,the rate of change may be calculated by using one of a moving average, amean statistic, a long term statistic, a short term statistic,derivatives, integrals, and/or and a known rate of change calculationmethod.

The rate of change tool 135 compares a reference rate of change of theparameter value (e.g., a historic rate of change of the parameter value)received from the SPC system 116 or calculated locally by the equipmentengineering system 130 to a current rate of change of the parametervalue of a current sample. In some embodiments, the reference rate ofchange of the parameter value is determined using one or more historicalrates of change of the parameter value from historical metrology datafor the operation on one or more previous samples in the fabricationprocess.

In some embodiments, based on the comparison of a current rate of changeof the parameter value and a reference rate of change of the parametervalue, the rate of change tool 135 identifies one or more violatinglocations each having an associated current rate of change of theparameter value that is greater than an associated reference rate ofchange of the parameter value.

The analysis of variance (ANOVA) tool 136 works with the rate of changetool 135 to identify the one or more violating locations. The ANOVA tool136 perform an ANOVA with the one or more of the historical rate ofchange of the parameter values to determine the reference rate of changeof the parameter value. In a further embodiment, the ANOVA result may beused to determine a threshold range of acceptable or non-violating ratesof change of the parameter value for a parameter at each of the one ormore locations for an operation on a current sample in a fabricationprocess. In some embodiments, the reference rate of change of theparameter value may be a range of non-violating values that arestatistically calculated from one or more historical rates of change ofthe parameter value by the ANOVA tool 136.

The pattern mapping tool 137 receives one or more violating locationsfrom one of the rate of change tool 135 and/or the ANOVA tool 136 andgenerates a sample pattern associated with the one or more violatinglocations on the current sample. In some embodiments, the patternmapping tool 137 applies a scaling (e.g., normalization of data points)for different violating locations based on trends or common patternsrecorded on previous samples.

The failure mode and effects analysis (FMEA) tool 138 identifies aninstance of abnormality of the fabrication process based on thecomparison of the current rate of change of the parameter value and thereference rate of change of the parameter value. In some embodiments,the FMEA tool 138 receives the sample pattern from the pattern mappingtool 137 and identifies the instance of abnormality based on the samplepattern.

The FMEA tool 138 may retrieve FMEA data 126 from data store 122. TheFMEA data 126 may include a list of known issues and root causes for thegiven equipment that has known symptoms associated with each. Thepattern sample data received by the FMEA tool 138 is applied to the listof known issues and a report is generated identifying common causes ofthe violating locations. For example, the FMEA tool 138 may receive asample pattern and identify a tool, machine, or operation of thefabrication process that is defective.

In some embodiments the FMEA tool 138 can be used with the processdependency tool 134 to identify a tool, machine, or process that isoperated upstream (e.g., an operation step that occurred prior to thecurrent manufacturing step of the same fabrication process) from thecurrent machine operation being performed on a current sample. Forexample, a current sample may have recently undergone a first operationby a first machine. In some embodiments, the combination of the processdependency tool 134 and the FMEA tool 138 can lookup past operations forthe sample, such as a second operation by a second machine or tool. TheFMEA tool 138 can use the sample pattern to identify that the secondmachine or second tool is the source of the abnormality.

Once the instance of abnormality is identified, the FMEA tool 138 canproceed by performing one of altering at least one of an operation of amachine or an implementation of a process associated with the instanceof abnormality and/or providing a graphical user interface (GUI)presenting a visual indicator of a machine or process associated withthe instance of abnormality. The GUI may be sent through network 140 andpresented on client device 128. In some embodiments, altering theoperation of the machine or the implementation of the process mayinclude sending instructions to the manufacturing execution system 102to alter machine operations 104 and/or process implementations 106associated with a process recipe or process dependency 108.

It should be noted that although manufacturing system 100 is depictedand described as a number of different systems and/or device, variousdevices may be combined together to perform the function of entitiesthat are depicted as separate on FIG. 1 . For example manufacturingexecution system 102 may be combined with metrology system 110 to carryout a fabrication process through manufacturing and metrologyoperations. In another example the equipment engineering system 130 maybe combined with SPC system 116 to both analyze the data from a SPCanalysis and a rate of change analysis using statistical process tool118 with the rate of change tool 135. In another example, data store 122may be stored on one or more of manufacturing execution system 102, themetrology system 110, the SPC system 116, the equipment engineeringsystem 130, and/or the client device 128. Additionally, althoughdepicted in FIG. 1 as using network 140 to communicate with each other,any of the manufacturing execution system 102, metrology system 110, SPCsystem 116, data store 122, client device 128, and/or equipmentengineering system 130 may be coupled directly and communicate betweeneach other without requiring network 140.

FIG. 2 is a block diagram illustrating a fabrication process 200 of amanufacturing system (e.g., manufacturing system 100 of FIG. 1 ),according to aspects of the present disclosure. The fabrication process200 may include one or more manufacturing operations 202A-B and one ormore metrology operations 204A-B. The manufacturing operations 202A-Bmay be performed by a manufacturing execution system (e.g.,manufacturing execution system 102 of FIG. 1 ). The metrology operations204A-B may be performed by a metrology system (e.g., metrology system110 of FIG. 1 ).

In some embodiments, the fabrication process 200 may include a series ofmanufacturing operations 202A-B and metrology operations 204A-B thatwhen completed create a fabricated sample with predeterminedcharacteristics and/or specifications. For example, a fabricationprocess to manufacture a multi-layered substrate may include variouslayer deposition operations that each deposit a layer onto to thesubstrate. The manufacturing operations 202A-B may be designed to becompleted in a predetermined order to create a multi-layered substratewith the correct order of layers.

Manufacturing operations 202A-B may include various methods offabricating a sample. For example, manufacturing operations may includeetching (e.g., dry etching, plasma etching, wet etching, chemicaletching, etc.), deposition (e.g., atomic layer deposition (ALD),chemical vapor deposition (CVD), or plasma enhanced versions thereof),patterning (e.g., photolithography, masking, etc.), microforming, and/orthe like.

Metrology operations 204A-B may include various methods and techniquesfor measuring a set of parameters 206A-D, 208A-D across one or morelocations on a fabricated sample at various stages of the fabricationprocess 200. The set of parameters 206A-D, 208A-D may include variousmeasured values associated with manufacturing operations 202A-B. Forexample, the set of parameters 206A-D, 208A-D may include measuring athickness of a channel, thickness of a layer or region, etch rate,resistivity, particle count, etc. of a sample. The measured values ofthe parameter may be indicative of a quality level of the performedmanufacturing operation. In some embodiments, the fabrication process200 measures 25-50 parameters across 12-20 locations across each sample.

In some embodiments, the parameters measured during a metrologyoperation 204A-B are indicative of a quality of the previousmanufacturing operation. Alternatively, the parameters measured during ametrology operation 204A-B may be indicative of a quality of the one ormore of the previous manufacturing operations. For example,manufacturing operations 202A and 202B may both be layer depositionoperations and a parameter (e.g., 208A) may measure the thickness of asubstrate. The thickness of the substrate during metrology operation204B may be affected by manufacturing operation 202A and 202B. Therelationships between parameters and operations may be stored as part ofa fabrication process recipe or process dependency (e.g., processdependencies 108 of FIG. 1 ). The process dependencies may track andmanage the effects of manufacturing operations 202A-B and downstreamprocesses (e.g., machine operations and process implementation that willbe performed in the future of the same fabrication process as thecurrent machine operation or process implementation), or processes thatwill occur after a specified manufacturing operation. Additionally, whena parameter value is processed or analyzed, process dependency data canprovide a link between prior operations and a current quality ofmanufacture of a fabrication sample at a specified stage of thefabrication process 200.

In some embodiments, manufacturing operations 202A-B and metrologyoperations 204A-B alternate throughout the fabrication process. Itshould be noted that in some embodiments multiple manufacturingoperations 202A-B may be performed between metrology operations 204A-B.Likewise, various metrology operations may be performed betweenmanufacturing operations 202A-B. The procedure and cadence of themanufacturing operations 202A-B and metrology operations 204A-B may bedependent on the specific fabrication process 200.

FIG. 3 illustrates a rate of parameter change distribution 300 ofvarious samples for an operation in a fabrication process, according toaspects of the present disclosure. As noted regarding FIG. 2 , afabrication process (e.g., fabrication process 200 of FIG. 2 ) mayinclude various manufacturing operations (e.g., manufacturing operations202A-B of FIG. 2 ) and various metrology operations (e.g., metrologyoperations 204A-B of FIG. 2 ) associated with the various manufacturingoperations. A fabrication process may be used to generate multiplesamples. As a result, values for a given parameter (e.g., parameters206A-D, 208A-D of FIG. 2 ) at each of one or more location across a setof sample is measured.

As shown in FIG. 3 , a parameter is measured at a designated location ona set of samples (e.g., sample 1, sample 2, sample 3, and sample 4) foran operation in a fabrication process. A rate of change tool (e.g., rateof change tool 135 of FIG. 1 ) may be used to process the raw metrologydata to determine a current rate of change of the parameter value and areference or historical rate of change of the parameter value.

A current rate of change of the parameter value can be calculated usinga current measurement of a current sample and a previous measurement ofa sample fabricated immediately previous to the current sample. In someembodiments, sample 1 may be fabricated prior to sample 2, sample 2 maybe fabricated prior to sample 3, sample 3 may be fabricated prior sample4, and sample 4 may be a current sample. A current sample rate of changemay include calculating a difference between a current sample and thesample immediately previous (e.g., sample 4 and sample 3, respectively).This current rate of change of the parameter value can be calculated foreach subsequent location on a sample. Further, the current rate ofchange of the parameter value can be calculated for all the parameters.

A reference rate of change of the parameter value can be calculatedusing historical metrology data (e.g., historical sample data 124). Forexample, previous samples (e.g., sample 1, sample 2, and/or sample 3)may be used to calculate a historical rate of change of the parametervalue for a parameter at a particular location on the samples. Forexample, the rate of change of the parameter value may be calculatedusing one of a moving average, a mean statistic, a long term statistic,a short term statistic, derivatives, integrals, and/or a known rate ofchange calculation method.

The current rate of change of the parameter value is compared to thereference rate of change of the parameter value. In one embodiment, thecomparison is performed by applying a statistical distribution to thereference rate of change of the parameter value to identify a variancebetween the current rate of change of the parameter value and thereference rate of change of the parameter value. For example, a mean anda standard deviation is calculated from the historical rates ofparameter values change and are compared against the current rate ofchange of the parameter value to identify how many standard deviationsthe current rate of change of the parameter value is from the mean ofthe one or more historical rates of change of a parameter value.

In some embodiments, a predetermined control limit or threshold limit(e.g., one standard deviation, two standard deviations, three standarddeviations, etc.) is used to identify locations that have abnormalcurrent rates of change of a parameter value. It should be noted thatthe control limit identified herein may be different that a conventionalSPC control limit. SPC controls identify a threshold range of measuredvalues, while the control limits identified herein identify a thresholdrange of a rate of change of the parameter value instead of anevaluation of a static measurement of sample. This difference may resultin identifying violating locations that otherwise might not be flaggedin a conventional SPC analysis.

In some embodiments, the reference rate of change of the parameter valuemay be calculated using a defined sampling window. For example, thereference rate of change of the parameter value may be generated by then number of samples generated prior to a current sample. For example, areference rate of change of the parameter value may be limited to samplefabricated within an amount of time (e.g., last 24 hours, week, month,etc.) or amount of samples (e.g., the last 10, 100, 1000, etc. samplesfabricated). It should also be noted that a reference rate of change ofthe parameter value is calculated at each location or site on a sample.The current rate of change of the parameter value is compared on alocation by location comparison across the surface of the sample.

In some embodiments, the violating locations on a sample may beindicated by a scoring system or by placing each location into a tier ordegree of violation rather than a binary indication of a violatingmeasurement. For example, a location may be classified into tiers suchas “passing”, “level one violation”, “level two violation”, “level threeviolation”, etc. The violation tiers may correspond to the variance ofthe current rate of change of the parameter value from the referencerate of change of the parameter value, at each location. For example, alocation that is less than one standard deviation off may be labeled as“passing,” a location that is between one and two standard deviationsmay be labeled as a “level-one violation,” a location that is betweentwo and three standard deviations may be labeled as a “level-twoviolation,” and so forth. Each violation may be assigned a weight or ascore than may be used to further establish a sample pattern, asdescribed in reference to FIG. 4 .

In some embodiments, a rate of change of the parameter value iscalculated for every sample transition. For example, referring to FIG. 3, a rate of change may be calculated from sample 1 to sample 2, fromsample 2 to sample 3, and from sample 3 to sample 4 to form a historicalrate of change of the parameter value dataset.

In some embodiments, a first analysis of variance (ANOVA) is calculatedon each process step, parameter, and location to identify variationwithin and across the samples in a particular process step and parameterto compare data wafer-to-wafer. In some embodiments, a second ANOVA iscalculated within and across the locations of a particular process stepand parameter to compare locations within a wafer. In some embodiments,a third ANOVA is calculated within and across the entire historical rateof change of the parameter value dataset (e.g., a combination of thefirst ANOVA and the second ANOVA). The first ANOVA, the second ANOVA,and/or the third ANOVA may then be used to generate a sample pattern fora current sample as well as previous historical samples for a givenparameter and process step of a fabrication process.

FIG. 4 illustrates an exemplary graphical user interface (GUI) 400 for asample pattern 404, according to aspects of the present disclosure. TheGUI 400 identifies one or more locations 402 on a sample, one or moresample patterns 404, a score 406 associated with each pattern, aconclusion 408, a site conclusion 410, and an overall conclusion 412.

The one or more locations 402 may include a first subset of locationsidentified as being in violation and a second subset of locationsidentified as passing or conforming. In some embodiments, the one ormore locations 402 may include a score quantifying the degree to whicheach location is in violations (e.g., passing, tier 1 violation, tier 2violation, etc.).

The sample pattern 404 may include various combinations of violationsthat a sample may experience during a fabrication process. These samplepatterns 404 may be based on common violation grouping on a sample. Forexample, a tool that is malfunctioning may incorrectly process locationson sample in close proximity to each other. In some embodiments, thesample pattern 404 are associated with violation patterns of processfunctions 502, parameters 504, potential fail modes 506, and potentialfail effects 508 of a failure mode and effects analysis (FMEA) asdescribed in association with FIG. 5 . For example, identifying a samplepattern 404 may include identifying edge sites or dishing problems inthe center of the wafer or quadrature. In another example, a samplepattern 404 may identify a particular location, zone, or behavior thatis specific to a process tool, machine, or operation.

As described previously (e.g. in association with ANOVA tool 136 of FIG.1 ), in some embodiments, a first analysis of variance (ANOVA) iscalculated on each process step, parameter, and location to identifyvariation within and across the samples in a particular process step andparameter to compare data wafer-to-wafer. In some embodiments, a secondANOVA is calculated within and across the locations of a particularprocess step and parameter to compare location within a wafer. In someembodiments, a thirst ANOVA is calculated within and across the entirehistorical rate of change of the parameter value dataset (e.g., acombination of the first ANOVA and the second ANOVA). The first ANOVA,the second ANOVA, and the third ANOVA may then be used to generate asample pattern for a current sample as well as previous historicalsamples for a given parameter and process step of a fabrication process.

Conclusion 408 is focused on the variation from wafer to wafer in thecurrent process. Conclusion 408 may include processing data associatedwith the first ANOVA. For example, the first ANOVA may identifyvariation within and across sample in a particular process step with andesignated parameter. Conclusions 408 may be provided based onprocessing logic comparing a current wafer against previous wafers andanalyzing a single parameter to determine if a pattern exists havingcommon parameters failing across different wavers.

Site conclusion 410 is focused on the variation that exits within thesample set on the current process. Site conclusions 410 may be providedby processing data associated with the second ANOVA. For example, thesecond ANOVA may identify a particular site and determine how variousparameters and samples performed at the particular site. Siteconclusions 410 may be provided based on processing logic comparing dataassociated with a particular site from a current sample and historicalsamples to determine if a pattern exists where the values of variousparameters and various samples are failing at a particular location oneach of the samples.

Overall conclusion is focused on identifying inherited variation comingfrom upstream processes that may be impacting the results of the currentprocess. Overall conclusions 412 may be provided by processing dataassociated with the third ANOVA. For example, the third ANOVA mayidentify patterns across the entire data set, including processdependencies for multiple locations across various samples. For example,overall conclusions 412 may be based on processing logic comparinghistorical metrology data (e.g. reference rates of parameter change)with metrology data (e.g. current rates of parameter change) at variouslocations and with various parameter to determine if a pattern existsbetween historical or inherited violations measurements from upstreammeasurements or of the historical metrology data and current violationsmeasurements of the current metrology data from data associated with thethird ANOVA.

FIG. 5 is a block diagram illustrating a process failure mode andeffects analysis (FMEA) 500, according to aspects of the presentdisclosure. The process FMEA includes process functions 502, parameters504, potential fail modes 506, and potential fail effects 508. Theprocess FMEA may be performed using a process FMEA tool (e.g., failuremode and effects analysis tool 138 of FIG. 1 ). The process FMEAreceives process functions 502 from one of a manufacturing system, ametrology system, and/or SPC system. The process function 502 may beorganized into process dependencies. For example, the process function502 may be stored in a dependency table, where the dependency identifiesparameters and locations on a sample affected by specific processfunctions 502. For example, the process function 502 may define themachine operations (e.g., lithography, etching, deposition, etc.).

The process FMEA receives data associated with parameters 504. In someembodiments, the parameter data may be received as a sample pattern, asdescribed in reference to FIG. 4 . Alternatively, the parameter data maybe received as a list of violating locations and parameters that are inviolation of a predetermined threshold range. The parameter data mayidentify locations having violating measurements. Violating measurementsmay include data that fails to meet a predetermined threshold such asthe rate of change of the parameter value of a measurement that isbeyond an acceptable reference rate of change of the parameter value fora given parameter at a specific location (as described in reference toFIG. 2 ).

The process FMEA 500 includes potential fail modes 506 and potentialfail effects 508 of a manufacturing system (e.g., manufacturing system100 of FIG. 1 ). For example, the FMEA table includes a list of knownissues and root causes for a given machine, tool, and/or piece ofequipment as well as the symptoms associated with each issue and rootcause. The process FMEA 500 may include logic linking the processfunctions 502 and/or parameters 504 to potential fail modes 506 and/orpotential fail effects 508. The process FMEA 500 may receive processedmetrology data and identify an instance of abnormality of themanufacturing system. For example, metrology data may be processed usingother embodiments described herein and received by the process FMEAtool. The process FMEA tool may analyze the data and identify one ormore defective machines, tools, and/or pieces of equipment as well asthe effects the defective tool has on the fabricated samples. Forexample, the process FMEA may identify a deposition tool as beingbroken, faulty, or in otherwise need of repair or replacement as well asdescribe the effects the current state of the deposition tool has onperforming its function (uneven layers, overly thin or thick layers,etc.) across the different locations on the sample.

In some embodiments, the process FMEA 500 may generate instructions foran equipment engineering system (e.g., equipment engineering system 130of FIG. 1 ) to alter one of an operation of a machine or animplementation of a process associated with an instance of abnormalityidentified by the process FMEA 500 for a fabrication process.

In some embodiments, the process FMEA 500 may generate instructions foran equipment engineering system (e.g., equipment engineering system 130of FIG. 1 ) to provide a graphical user interface (GUI) to present avisual indicator of a machine or a process associated with the instanceof abnormality. For example, the visual indicator may be sent to aclient device (e.g., client device 128 of FIG. 1 ) for an engineer oroperator of the fabrication process to manually alter a manufacturingexecution system carrying out the fabrication process.

In some embodiments, machine learning (ML) algorithms that generate oneor more trained machine learning models, deep ML algorithms, and/orother signal processing algorithms for analyzing parameter data can beused to determine potential fail modes 506 and potential fail effects508 of a manufacturing system. These models, analysis, and/or algorithmscan be used to calculate, predict, and evaluate combinations of processfunctions 502 and parameters 504 to predict and identify potential failmodes 506 and potential fail effects 508. In some embodiments, trainingdata to train a ML model may be obtained by a metrology system (e.g.,metrology system 110 of FIG. 1 ) or historical sample data (e.g.,historical sample data 124 of FIG. 1 ).

One type of machine learning model that may be used is an artificialneural network, such as a deep neural network. Artificial neuralnetworks generally include a feature representation component with aclassifier or regression layers that map features to a desired outputspace. A convolutional neural network (CNN), for example, hosts multiplelayers of convolutional filters. Pooling is performed, andnon-linearities may be addressed, at lower layers, on top of which amulti-layer perceptron is commonly appended, mapping top layer featuresextracted by the convolutional layers to decisions (e.g. classificationoutputs). Deep learning is a class of machine learning algorithms thatuse a cascade of multiple layers of nonlinear processing units forfeature extraction and transformation. Each successive layer uses theoutput from the previous layer as input. Deep neural networks may learnin a supervised (e.g., classification) and/or unsupervised (e.g.,pattern analysis) manner. Deep neural networks include a hierarchy oflayers, where the different layers learn different levels ofrepresentations that correspond to different levels of abstraction. Indeep learning, each level learns to transform its input data into aslightly more abstract and composite representation. In processabnormality application, for example, the raw input may be current andhistorical metrology data; the first representational layer may abstractthe location and parameter values; the second layer may compose andencode basic violating locations; the third layer may encode samplepatterns; and the fourth layer may recognize and match the data topotential fail modes and potential fail effects of a fabricationprocess. Notably, a deep learning process can learn which features tooptimally place in which level on its own. The “deep” in “deep learning”refers to the number of layers through which the data is transformed.More precisely, deep learning systems have a substantial CreditAssignment Path (CAP) depth. The CAP is the chain of transformationsfrom input to output. CAPs describe potentially causal connectionsbetween input and output. For a feedforward neural network, the depth ofthe CAPs may be that of the network and may be the number of hiddenlayers plus one. For recurrent neural networks, in which a signal maypropagate through a layer more than once, the CAP depth is potentiallyunlimited.

In one embodiment, a neural network is trained using a training datasetthat includes multiple data points, where each data point includesparameter 504, a location on the sample, and a process function 502.Each training data point may additionally include or be associated witha potential fail mode 506 and/or a potential fail effect 508. The neuralnetwork may be trained using the training dataset to receive an input ofa process function, location on a sample, and parameter and to output anidentification of an instance of abnormality of a manufacturing system.Alternatively or additionally, the neural network may incorporate use ofa sample pattern as either input or output of the training dataset asdescribed in reference to FIG. 4 .

FIG. 6 is a flow diagram of a method 600 for identifying an instance ofabnormality of a fabrication process, according to aspects of thepresent disclosure. For simplicity of explanation, method 600 isdepicted and described as a series of acts. However, acts in accordancewith this disclosure can occur in various orders and/or concurrentlywith other acts not presented and described herein. Furthermore, not allillustrated acts may be performed to implement method 600 in accordancewith the disclosed subject matter. In addition, those skilled in the artwill understand and appreciate that method 600 could alternatively berepresented as a series of interrelated states via a state diagram orevents.

Referring to FIG. 6 , at block 601 the processing logic receives currentmetrology data for an operation on a current sample in a fabricationprocess. The metrology data may include a parameter value at each of oneor more locations on the current sample. The current metrology data maybe received from a metrology system (e.g., metrology system 110 of FIG.1 ), a manufacturing execution system (manufacturing system 102 of FIG.1 ), or an SPC system (e.g., SPC system 116 of FIG. 1 ). The metrologydata may be associated with a machine operation (e.g., machine operation104 of FIG. 1 ) and/or a process implementation (e.g., processimplementation 106 of FIG. 1 ) of a fabrication process (e.g.,fabrication process 200 of FIG. 2 ).

At block 602, the processing logic obtains a reference rate of change ofthe parameter value of a parameter for each of the one or morelocations. In some embodiments, the reference rate of change of theparameter value is determined using one or more historical rates ofchange of a parameter value from historical metrology data for theoperation on one or more previous samples in the fabrication process.Alternatively or additionally, the reference rate of change may beobtained through any of the disclosed embodiments disclosed herein(e.g., calculating the reference rate of change of the parameter valueas described in reference to FIGS. 1 and 3 ). In some embodiments, thereference rate of change is calculated by an SPC system (e.g, SPC system116 of FIG. 1 ) or a rate of change tool (e.g., rate of change tool 135of FIG. 1 ).

At block 603, the processing logic determines a current rate of changeof the parameter value for each of the one or more locations. Thecurrent rate of change of the parameter value may be associated with thecurrent sample. Determining the current rate of change of the parametervalue associated with the current sample may include any or all of thedisclosed statistical and data processing techniques disclosed herein(e.g., the statistical and data process techniques disclosed inassociation with FIG. 3 ).

At block 604, the processing logic compares the current rate of changeof the parameter value for each of the one or more locations. In someembodiments, the processing logic may further identify one or moreviolating locations each having an associated current rate of change ofthe parameter value that is greater than an associated reference rate ofchange of the parameter value. Identifying violating measurement mayinclude any or all of the disclosed statistical and data processingtechniques disclosed herein (e.g., the statistical and data processtechniques disclosed in reference to FIG. 3 ).

At block 605, the processing logic identifies an instance of abnormalityof the fabrication process based on the comparison of the current rateof change of the parameter value and the reference rate of change of theparameter value. In some embodiments, the process may further retrieveone or more process dependencies of the fabrication process and identifythe instance of abnormality of the fabrication process based on one ormore of the process dependencies. The instance of abnormality mayinclude a defective machine, tool, or piece of equipment or animproperly implemented process operation. Identifying the instance ofabnormality may include identifying the source (e.g., machine, tool,equipment, process, etc.) of the abnormality or the effects of theabnormality (e.g., damaged samples, potential damage to other machines,parameters that are likely not to meet specification requirements).

At block 606, optionally, the processing logic alters at least one of anoperation of a machine or an implementation of a process associated withthe instance of abnormality. Altering the operation of a machine orimplementation of a process may involve shutting down a machine, haltinga process, or indicating an error and awaiting a user input to stop oradjust a mode of operation prior to resuming operation.

In some embodiments, the processing logic may provide a graphical userinterface (GUI) presenting a visual indicator of a machine or a processassociated with the instance of abnormality. For example, a measurementviolation tool (e.g., measurement violation tool 132 of FIG. 1 ) mayidentify an instance of abnormality (e.g., a defective machine, tool,process implementation) and prepare for presentation on a client device(e.g. client device 128 of FIG. 1 ), a visual indicator of the instanceof abnormality. A user (e.g., engineer or manufacturing system operator)may use the indicator to direct remedial action to correct for theabnormality.

In some embodiments, the processing logic may further identify one ormore violating locations each having an associated current rate ofchange of the parameter value that is greater than an associatedreference rate of change of the parameter value. In a furtherembodiment, the processing logic may further determine a sample patternassociated with the one or more violating locations and identify theinstance of abnormality based on the sample pattern.

FIG. 7 illustrates a block diagram of an example computing device 700capable of identifying instances of abnormality of a fabricationprocess. In various illustrative examples, various components of thecomputing device 700 may represent various components of themanufacturing execution system 102, metrology system 110, SPC system116, data store 122, client device 128, equipment engineering system 130and network 140 illustrated in FIG. 1 .

Example computing device 700 may be connected to other computer devicesin a LAN, an intranet, an extranet, and/or the Internet. Computingdevice 700 may operate in the capacity of a server in a client-servernetwork environment. Computing device 700 may be a personal computer(PC), a set-top box (STB), a server, a network router, switch or bridge,or any device capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that device. Further,while only a single example computing device is illustrated, the term“computer” shall also be taken to include any collection of computersthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methods discussed herein.

Example computing device 700 may include a processing device 702 (alsoreferred to as a processor or CPU), a main memory 704 (e.g., read-onlymemory (ROM), flash memory, dynamic random access memory (DRAM) such assynchronous DRAM (SDRAM), etc.), a static memory 706 (e.g., flashmemory, static random access memory (SRAM), etc.), and a secondarymemory (e.g., a data storage device 718), which may communicate witheach other via a bus 730.

Processing device 702 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, processing device 702 may be a complex instructionset computing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,processor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 702may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. In accordance with one or more aspects of the presentdisclosure, processing device 702 may be configured to executeinstructions implementing method 600 illustrated in FIG. 6 .

Example computing device 700 may further comprise a network interfacedevice 708, which may be communicatively coupled to a network 720.Example computing device 700 may further comprise a video display 710(e.g., a liquid crystal display (LCD), a touch screen, or a cathode raytube (CRT)), an alphanumeric input device 712 (e.g., a keyboard), acursor control device 714 (e.g., a mouse), and an acoustic signalgeneration device 716 (e.g., a speaker).

Data storage device 718 may include a machine-readable storage medium(or, more specifically, a non-transitory machine-readable storagemedium) 728 on which is stored one or more sets of executableinstructions 722. In accordance with one or more aspects of the presentdisclosure, executable instructions 722 may comprise executableinstructions associated with executing method 600 illustrated in FIG. 6.

Executable instructions 722 may also reside, completely or at leastpartially, within main memory 704 and/or within processing device 702during execution thereof by example computing device 700, main memory704 and processing device 702 also constituting computer-readablestorage media. Executable instructions 722 may further be transmitted orreceived over a network via network interface device 708.

While the computer-readable storage medium 728 is shown in FIG. 7 as asingle medium, the term “computer-readable storage medium” should betaken to include a single medium or multiple media (e.g., a centralizedor distributed database, and/or associated caches and servers) thatstore the one or more sets of operating instructions. The term“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine that cause the machine to perform any one ormore of the methods described herein. The term “computer-readablestorage medium” shall accordingly be taken to include, but not belimited to, solid-state memories, and optical and magnetic media.

Some portions of the detailed descriptions above are presented in termsof algorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, as apparent from the followingdiscussion, it is appreciated that throughout the description,discussions utilizing terms such as “identifying,” “determining,”“storing,” “adjusting,” “causing,” “returning,” “comparing,” “creating,”“stopping,” “loading,” “copying,” “throwing,” “replacing,” “performing,”or the like, refer to the action and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage, transmission or display devices.

Examples of the present disclosure also relate to an apparatus forperforming the methods described herein. This apparatus may be speciallyconstructed for the required purposes, or it may be a general purposecomputer system selectively programmed by a computer program stored inthe computer system. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding optical disks, compact disc read only memory (CD-ROMs), andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), erasable programmable read-only memory (EPROMs),electrically erasable programmable read-only memory (EEPROMs), magneticdisk storage media, optical storage media, flash memory devices, othertype of machine-accessible storage media, or any type of media suitablefor storing electronic instructions, each coupled to a computer systembus.

The methods and displays presented herein are not inherently related toany particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct a more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear as set forth in thedescription below. In addition, the scope of the present disclosure isnot limited to any particular programming language. It will beappreciated that a variety of programming languages may be used toimplement the teachings of the present disclosure.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other implementation exampleswill be apparent to those of skill in the art upon reading andunderstanding the above description. Although the present disclosuredescribes specific examples, it will be recognized that the systems andmethods of the present disclosure are not limited to the examplesdescribed herein, but may be practiced with modifications within thescope of the appended claims. Accordingly, the specification anddrawings are to be regarded in an illustrative sense rather than arestrictive sense. The scope of the present disclosure should,therefore, be determined with reference to the appended claims, alongwith the full scope of equivalents to which such claims are entitled.

1. A method, comprising: receiving, by at least one processing device,current metrology data for an operation on a current sample in afabrication process, the current metrology data comprising a currentvalue for a parameter at each of one or more locations on the currentsample; determining, by the at least one processing device, a currentrate of change of the parameter value for each of the one or morelocations, the current rate of change of the parameter value associatedwith the current sample; identifying, by the at least one processingdevice, one or more violating locations each having an associatedcurrent rate of change of the parameter value that is greater than anassociated reference rate of change of the parameter value; identifying,by the at least one processing device, an instance of abnormality of thefabrication process based on the one or more violating locations; andcausing performance of a corrective action associated with fabricationprocess equipment based on the instance of abnormality.
 2. The method ofclaim 1, further comprising: altering, by the at least one processingdevice, at least one of an operation of a machine or an implementationof a process associated with the instance of abnormality.
 3. The methodof claim 1, wherein at least one of the associated reference rate ofchange of the parameter value is determined by performing an analysis ofvariance (ANOVA) with one or more historical rates of change of theparameter value.
 4. The method of claim 1, further comprising:determining, by the at least one processing device, a sample patternassociated with the one or more violating locations; and identifying, bythe at least one processing device, the instance of abnormality based onthe sample pattern.
 5. The method of claim 1, further comprising:retrieving, by the at least one processing device, one or more processdependencies of the fabrication process; and identifying, by the atleast one processing device, the instance of abnormality of thefabrication process based on one or more of the process dependencies. 6.The method of claim 1, further comprising: providing, by the at leastone processing device, a graphical user interface (GUI) presenting avisual indicator of a machine or a process associated with the instanceof abnormality.
 7. The method of claim 1, further comprising: obtaining,by the at least one processing device, the associated reference rate ofchange of the parameter value of the parameter for each of the one ormore locations, wherein the associated reference rate of change of theparameter value is determined using one or more historical rates ofchange of the parameter value from historical metrology data for theoperation on one or more previous samples in the fabrication process. 8.A system comprising: a memory; and at least one processing device,coupled to the memory, to perform operations comprising: receivingcurrent metrology data for an operation on a current sample in afabrication process, the current metrology data comprising a currentvalue for a parameter at each of one or more locations on the currentsample; determining a current rate of change of the parameter value foreach of the one or more locations, the current rate of change of theparameter value associated with the current sample; identifying one ormore violating locations each having an associated current rate ofchange of the parameter value that is greater than an associatedreference rate of change of the parameter value; identifying an instanceof abnormality of the fabrication process based on the one or moreviolating locations; and causing performance of a corrective actionassociated with fabrication process equipment based on the instance ofabnormality.
 9. The system of claim 8, the operations furthercomprising: altering at least one of an operation of a machine or animplementation of a process associated with the instance of abnormality.10. The system of claim 8, wherein at least one of the associatedreference rate of change of the parameter value is determined byperforming an analysis of variance (ANOVA) with one or more historicalrates of change of the parameter value.
 11. The system of claim 8, theoperations further comprising: determining a sample pattern associatedwith the one or more violating locations; and identifying the instanceof abnormality based on the sample pattern.
 12. The system of claim 8,the operations further comprising: retrieving one or more processdependencies of the fabrication process; and identifying the instance ofabnormality of the fabrication process based on one or more of theprocess dependencies.
 13. The system of claim 8, the operations furthercomprising: providing a graphical user interface (GUI) presenting avisual indicator of a machine or a process associated with the instanceof abnormality.
 14. The system of claim 8, the operations furthercomprising: obtaining the associated reference rate of change of theparameter value of the parameter for each of the one or more locations,wherein the associated reference rate of change of the parameter valueis determined using one or more historical rates of change of theparameter value from historical metrology data for the operation on oneor more previous samples in the fabrication process.
 15. Anon-transitory machine-readable storage medium comprising instructionsthat, when executed by at least one processing device, cause the atleast one processing device to perform operations comprising: receivingcurrent metrology data for an operation on a current sample in afabrication process, the current metrology data comprising a currentvalue for a parameter at each of one or more locations on the currentsample; determining a current rate of change of the parameter value foreach of the one or more locations, the current rate of change of theparameter value associated with the current sample; identifying one ormore violating locations each having an associated current rate ofchange of the parameter value that is greater than an associatedreference rate of change of the parameter value; identifying an instanceof abnormality of the fabrication process based on the one or moreviolating locations; and causing performance of a corrective actionassociated with fabrication process equipment based on the instance ofabnormality.
 16. The non-transitory machine-readable storage medium ofclaim 15, wherein at least one of the associated reference rate ofchange of the parameter value is determined by performing an analysis ofvariance (ANOVA) with one or more historical rates of change of theparameter value.
 17. The non-transitory machine-readable storage mediumof claim 15, the operations further comprising: determining a samplepattern associated with the one or more violating locations; andidentifying the instance of abnormality based on the sample pattern. 18.The non-transitory machine-readable storage medium of claim 15, theoperations further comprising: retrieving one or more processdependencies of the fabrication process; and identifying the instance ofabnormality of the fabrication process based on one or more of theprocess dependencies.
 19. The non-transitory machine-readable storagemedium of claim 15, the operations further comprising: providing agraphical user interface (GUI) presenting a visual indicator of amachine or a process associated with the instance of abnormality. 20.The non-transitory machine-readable storage medium of claim 15, theoperations further comprising: obtaining the associated reference rateof change of the parameter value of the parameter for each of the one ormore locations, wherein the associated reference rate of change of theparameter value is determined using one or more historical rates ofchange of the parameter value from historical metrology data for theoperation on one or more previous samples in the fabrication process.